code: def _get_training_args(self, args):
      if self.device.type != "cuda":
          if args.fp16:
              ValueError(
                  "fp16 is only available when CUDA/ a GPU is being used. "
              )
  
      if self._type == "tt":
          arg_class = Seq2SeqTrainingArguments
      else:
          arg_class = TrainingArguments
  
      deepspeed = self.__get_deepspeed_config(args, True)
  
      return arg_class(
          deepspeed=deepspeed,
          output_dir=args.output_dir,
          learning_rate=args.learning_rate,
          weight_decay=args.weight_decay,
          num_train_epochs=args.num_train_epochs,
          report_to=["none"]
          if len(args.report_to) == 0
          else args.report_to,
          save_strategy="steps" if args.save_steps > 0 else "no",
          save_steps=args.save_steps,
          evaluation_strategy="steps" if args.eval_steps > 0 else "no",
          eval_steps=args.eval_steps,
          logging_strategy="steps" if args.logging_steps > 0 else "no",
          logging_steps=args.logging_steps,
          per_device_train_batch_size=args.batch_size,
          per_device_eval_batch_size=args.batch_size,
          fp16=args.fp16,
          use_mps_device=True if self.device.type == "mps" else False,
          run_name=args.run_name,
      )
  
imports: import logging
  import os
  import tempfile
  from typing import Union
  from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
  import torch
  from transformers import AutoConfig, AutoModel, AutoTokenizer, Seq2SeqTrainer, Seq2SeqTrainingArguments, Trainer, TrainingArguments
  from happytransformer.args import EvalArgs, TrainArgs
  from happytransformer.fine_tuning_util import EvalResult, FistStep, ZERO_2_SETTINGS, ZERO_3_SETTINGS
label_desc: Use image node_id 12 for calling the HappyTransformer obj's underlying member method code with example usage: obj._get_training_args(args) and returns: arg_class